Accelerating the convergence of a decision-based algorithm for blind equalization of QAM signals

Recently, we proposed a decision-based algorithm for blind equalization that performs similarly to a supervised algorithm in steady-state, independently of the QAM order. Using this algorithm, the usual switching to the decision-directed mode may be avoided. Additionally, a low-cost scheme based on the neighborhood of the estimated symbol was used to improve its convergence rate. In this paper, we interpret this scheme from a probability density function fitting perspective. When the uncertainty on the estimated symbol is high, simulations show that the neighborhood plays an important role for speeding up the convergence. On the other hand, to ensure a low mean-square error, the neighborhood must be disregarded in steady-state.